Econ Game Theory

What it does

When your agent uses it

• The user needs to model competitive decisions
• Predict rival behavior
• Design incentive mechanisms
• Evaluate cooperation vs competition scenarios — even if they say what will our competitor do

Example prompts

• “what will our competitor do”
• “should we cooperate or compete”
• “how do we set up the right incentives”
• “/econ-game-theory”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Identify players and their available strategies
2. 2Build the payoff matrix (simultaneous) or game tree (sequential)
3. 3Check for dominant strategies per player
4. 4Find Nash Equilibrium — where best responses intersect
5. 5For sequential games: apply backward induction from terminal nodes
6. 6Evaluate efficiency — is the NE Pareto optimal? If not, flag cooperation opportunity
7. 7The resulting path is the Subgame Perfect Equilibrium

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Econ Game Theory loads about 1.1k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 388 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~114

When it runs · the whole SKILL.md, loaded when a task matches

~1.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~6.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 388 words, ~1,122 tokens.

name

econ-game-theory

description

Apply basic game theory concepts including Nash equilibrium, dominant strategies, and the Prisoner's Dilemma to analyze strategic interactions. Use this skill when the user needs to model competitive decisions, predict rival behavior, design incentive mechanisms, or evaluate cooperation vs competition scenarios — even if they say 'what will our competitor do', 'should we cooperate or compete', or 'how do we set up the right incentives'.

metadata.category

WP-17 經濟學院

metadata.tags

economics, game-theory, strategy

IRON LAW: Define Players, Strategies, and Payoffs BEFORE Analyzing

Every game requires three elements explicitly defined:
1. Players — who are the decision-makers?
2. Strategies — what choices does each player have?
3. Payoffs — what does each player get for each combination of choices?

Analyzing a "game" without a payoff matrix is just storytelling.

# Game Theory Analysis: {Situation}

## Game Setup
- Players: {list}
- Strategies: Player 1: {A, B}, Player 2: {X, Y}
- Type: Simultaneous / Sequential

## Payoff Matrix (simultaneous) or Game Tree (sequential)

|  | Player 2: X | Player 2: Y |
|---|---|---|
| Player 1: A | (a1, a2) | (b1, b2) |
| Player 1: B | (c1, c2) | (d1, d2) |

## Analysis
- Dominant strategies: {if any}
- Nash Equilibrium: {strategy combination, payoffs}
- Pareto optimal? {yes/no — if no, explain the cooperation opportunity}

## Strategic Implications
{What should each player do? What mechanism could improve outcomes?}

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in econ-game-theory of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/mechanism-design.md
• references/repeated-games.md

Open the folder on GitHubat commit 4e7f4f8